Zobrazeno 1 - 10
of 195
pro vyhledávání: '"Aurélie, C."'
Publikováno v:
BMC Bioinformatics, Vol 24, Iss 1, Pp 1-15 (2023)
Abstract Background We consider two key problems in genomics involving multiple traits: multi-trait genome wide association studies (GWAS), where the goal is to detect genetic variants associated with the traits; and multi-trait genomic selection (GS
Externí odkaz:
https://doaj.org/article/2299f10a29854727bcc471b16fb3a513
We study the training of regularized neural networks where the regularizer can be non-smooth and non-convex. We propose a unified framework for stochastic proximal gradient descent, which we term ProxGen, that allows for arbitrary positive preconditi
Externí odkaz:
http://arxiv.org/abs/2007.07484
Recent theoretical works based on the neural tangent kernel (NTK) have shed light on the optimization and generalization of over-parameterized networks, and partially bridge the gap between their practical success and classical learning theory. Espec
Externí odkaz:
http://arxiv.org/abs/2007.00884
Adaptive gradient approaches that automatically adjust the learning rate on a per-feature basis have been very popular for training deep networks. This rich class of algorithms includes Adagrad, RMSprop, Adam, and recent extensions. All these algorit
Externí odkaz:
http://arxiv.org/abs/1905.10757
Autor:
Bernhard, Katie P.1 (AUTHOR) kpb5766@psu.edu, Shapiro, Aurélie C.2 (AUTHOR) aurelie.shapiro@fao.org, d'Annunzio, Rémi2 (AUTHOR) remi.dannunzio@fao.org, Kabuanga, Joël Masimo3,4 (AUTHOR) joel.masimo@uqat.ca
Publikováno v:
Remote Sensing. Jan2024, Vol. 16 Issue 1, p204. 22p.
Akademický článek
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Publikováno v:
Remote Sensing, Vol 16, Iss 1, p 204 (2024)
The forests of Central Africa constitute the continent’s largest continuous tract of forest, maintained in part by over 200 protected areas across six countries with varying levels of restriction and enforcement. Despite protection, these Central A
Externí odkaz:
https://doaj.org/article/08a70cfe85aa4965ad309994739497a2
Autor:
Lozano, Aurélie C.1 (AUTHOR), Ding, Hantian2 (AUTHOR), Abe, Naoki1 (AUTHOR), Lipka, Alexander E.3 (AUTHOR) alipka@illinois.edu
Publikováno v:
BMC Bioinformatics. 10/26/2023, Vol. 24 Issue 1, p1-15. 15p.
Autor:
Yu, Ming, Thompson, Addie M., Ramamurthy, Karthikeyan Natesan, Yang, Eunho, Lozano, Aurélie C.
Inferring predictive maps between multiple input and multiple output variables or tasks has innumerable applications in data science. Multi-task learning attempts to learn the maps to several output tasks simultaneously with information sharing betwe
Externí odkaz:
http://arxiv.org/abs/1710.01788
Sparse mapping has been a key methodology in many high-dimensional scientific problems. When multiple tasks share the set of relevant features, learning them jointly in a group drastically improves the quality of relevant feature selection. However,
Externí odkaz:
http://arxiv.org/abs/1705.04886